---
title: 'Improving language models by retrieving from trillions of tokens'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2112-04426-retro.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, et al. (28 authors), Improving language models by retrieving from trillions of tokens (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked; code not run.'
category: arxiv
topic_area: 'ai-research: RAG'
tags: arxiv, retrieval, language-models
arxiv_id: 2112.04426
alias: 2112.04426-retro
sources:
  - id: S1
    title: 'Improving language models by retrieving from trillions of tokens'
    publisher: arXiv
    authors: 'Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, et al. (28 authors)'
    url: https://arxiv.org/abs/2112.04426
    version_read: 'v3 (7 Feb 2022)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# Improving language models by retrieving from trillions of tokens

## Summary
RETRO (Retrieval-Enhanced Transformer) conditions a language model on document chunks retrieved from a large corpus by local similarity with preceding tokens. With a 2 trillion token database, the authors report performance comparable to GPT-3 and Jurassic-1 on the Pile, despite using 25 times fewer parameters. RETRO combines a frozen Bert retriever, a differentiable encoder, and chunked cross-attention, and can also RETROfit pretrained transformers. [S1]

## Claims
- id: C1
  claim: 'RETRO retrieves document chunks from a large corpus based on local similarity with preceding tokens.'
  source: S1
  evidence: "document chunks retrieved from a large corpus"
- id: C2
  claim: 'With a 2 trillion token database, they report performance comparable to GPT-3 and Jurassic-1 on the Pile, using 25 times fewer parameters.'
  source: S1
  evidence: "comparable performance to GPT-3 and Jurassic-1 on the Pile"
- id: C3
  claim: 'RETRO combines a frozen Bert retriever, a differentiable encoder, and chunked cross-attention.'
  source: S1
  evidence: "RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism"

## Verification
- Code available: No official RETRO code URL was named as this paper's release on the abs page or HTML. Cited third-party tools such as FiD were not treated as this paper's code.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live notes 2310-11511-self-rag and genai-rag-embeddings-vector-search. This is the paper-verification note for RETRO itself.
- Limits: Comments note a Table 14 number fix; this note uses the abstract and key description. The retrieval database is not public here.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 8 Dec 2021 (v1); last revised 7 Feb 2022 (v3). Sources: [S1] https://arxiv.org/abs/2112.04426, v3 (7 Feb 2022). checked_on: 2026-11-11.

## When to apply
Use when comparing retrieval at training and inference scale with later RAG methods, especially when retrieval is meant to substitute for some model size.

## Facts
- id: F1
  claim: 'RETRO conditions generation on document chunks retrieved from a large corpus.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors report that with a 2 trillion token database, RETRO matches GPT-3 and Jurassic-1 on the Pile with 25 times fewer parameters.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They describe both training RETRO from scratch and RETROfitting pretrained transformers with retrieval.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2112.04426, https://arxiv.org/abs/2112.04426. License: CC BY 4.0. v3 (7 Feb 2022). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).